CN109961198A - Related information generation method and device - Google Patents
Related information generation method and device Download PDFInfo
- Publication number
- CN109961198A CN109961198A CN201711420197.4A CN201711420197A CN109961198A CN 109961198 A CN109961198 A CN 109961198A CN 201711420197 A CN201711420197 A CN 201711420197A CN 109961198 A CN109961198 A CN 109961198A
- Authority
- CN
- China
- Prior art keywords
- sales volume
- volume data
- end article
- curve
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of related information generation method and devices, are related to Internet technical field.One specific embodiment of this method includes: to obtain the sales volume data of end article current time；Based on the sales volume data and preset estimation model, the turnover information of the end article is determined；According to the turnover information, the related information of the end article is generated.The embodiment can accurately determine the turnover information of commodity, and the related information of the commodity is generated according to the turnover information, can reasonably manage inventory according to the related information, and then improve whole efficiency of operation, reduce warehouse cost and cost of capital；Manual intervention is reduced, human cost is reduced.
Description
Technical field
The present invention relates to Internet technical field more particularly to a kind of related information generation method and devices.
Background technique
In stock control, from large-scale retailer or the storehouse of electric business by unsalable merchandise return to supplier be it is a kind of very
Common stock control means.In general, normal sale goes out compared to returning goods to supplier to be bigger behavior of making a profit.But
It is that, since commodity storage is in storehouse, will lead to two main costs and generate: warehouse cost and cost of capital.Thus, it fits
When, suitable return of goods behavior can directly reduce warehouse cost and release cost of capital, so that reaching reduces cost, in turn
Increase the purpose of profit.As company manager, need to return goods in the appropriate time with appropriate quantity.
Currently, the method for the popular unsalable inventory of calculating be rule of thumb return goods or with supplier sign a contract when
One preparation cycle T of setting is waited, referring to average daily sales volume of the commodity within the past period, base stock is calculated, when existing
When stockpile number is higher than base stock, return of goods behavior will be triggered.Fundamental formular is following (by taking storehouse A, commodity H as an example):
Base stock=(preparation cycle of commodity H) × (commodity H history 60 light in storehouse A of the commodity H in storehouse A
Equal sales volume)；
Commodity H A storehouse unsalable inventory=max [(commodity H A storehouse on-hand inventory-commodity H in A storehouse
Base stock), 0].
Specifically, the process of this method is as follows:
1. being directed to the commodity (by taking commodity H as an example) of each category, stock week is signed according to business personnel's experience and supplier
Phase T；
2. counting the storehouse quantity n of enterprise, such as n=4, respectively storehouse A, storehouse B, storehouse C and storehouse D；
3. calculating commodity H in the average daily sales volume of different storehouses according to history sales volume data, indicated with P；
4. calculating commodity according to the following formula in the base stock of certain storehouse:
Base stock=P × T；
5. calculating unsalable inventory in the on-hand inventory of storehouse according to commodity H, formula is as follows:
Unsalable inventory=max [(on-hand inventory-base stock), 0]
I.e. all on-hand inventories greater than base stock are all unsalable inventories；
6. counting commodity in the unsalable inventory in each warehouse, corresponding return of goods behavior is given.
In realizing process of the present invention, at least there are the following problems in the prior art for inventor's discovery:
The sale that prior art does not account for seasonal merchandise has the characteristics that periodic, and the sales volume in busy season is far high
In dull season, usage history daily measures that will lead to dull season base stock excessively high, causes a large amount of unsalable, is unfavorable for stock control and cost
Management.
Due to large-scale retailer or the service coverage of the electric business enterprise whole nation, there is storehouse in different regions, same commodity exist
The sales cycle property of different regions is also inconsistent (such as air-conditioning enters the period of slack sales in the north in advance), and annual climate change
Also it has any different, by artificial control or being controlled based on history sales volume setting preset parameter all can not accurately determine certain commodity in the whole nation
When different regions enter the period of slack sales, also just can not accurately determine the return of goods time of commodity.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of related information generation method and device, which can be quasi-
The turnover information for determining commodity, the related information of the commodity is generated according to the turnover information, can be closed according to the related information
The management inventory of reason, and then whole efficiency of operation is improved, warehouse cost and cost of capital are reduced, manual intervention, drop are reduced
Low human cost, and improve intelligent level.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of related information generation method is provided,
It include: the sales volume data for obtaining end article current time；Based on the sales volume data and preset estimation model, determine described in
The turnover information of end article；According to the turnover information, the related information of the end article is generated.
Optionally, it is being based on the sales volume data and preset estimation model, is determining the turnover information of the end article
Before, the method also includes: obtain the end article history sales volume data and return goods record；According to the history sales volume
Data and record of returning goods, are trained preset estimation model.
Optionally, according to the history sales volume data and record of returning goods, being trained to preset estimation model includes: root
According to the history sales volume data, when building m- accumulative sales volume sequence；To it is described when m- accumulative sales volume sequence carry out curve fitting,
Determine iunction for curve；Based on it is described when m- accumulative sales volume sequence, determine the multiple groups parameter of the iunction for curve,
In, the parameter is corresponding with the time；It is recorded according to the multiple groups parameter and the return of goods, preset estimation model is instructed
Practice.
Optionally, described to be based on the sales volume data and preset estimation model, determine the transferring mail of the end article
Breath includes: to determine parameter corresponding with the current time according to the sales volume data；Parameter input preset is estimated
Model is counted, with the turnover information of the determination end article.
Optionally, the iunction for curve is shown below:
Y=axb
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are ginseng to be determined
Number.
Optionally, the multiple groups parameter of the iunction for curve is determined according to following process: by the iunction for curve
Logarithm conversion is done, the iunction for curve is converted into linear function；Based on it is described when m- accumulative sales volume sequence and described
Linear function determines the multiple groups parameter of the iunction for curve using least square method.
Optionally, the preset estimation model is Logic Regression Models.
To achieve the above object, according to an aspect of an embodiment of the present invention, a kind of related information generating means are provided,
It include: sales volume data acquisition module, for obtaining the sales volume data of end article current time；Information determination module is had enough to meet the need, is used
In being based on the sales volume data and preset estimation model, the turnover information of the end article is determined；Related information generates mould
Block, for generating the related information of the end article according to the turnover information.
Optionally, described device further includes model training module, is used for: obtaining the history sales volume data of the end article
It is recorded with returning goods；According to the history sales volume data and record of returning goods, preset estimation model is trained.
Optionally, the model training module is also used to: according to the history sales volume data, when building m- accumulative sales volume
Sequence；To it is described when m- accumulative sales volume sequence carry out curve fitting, determine iunction for curve；Based on it is described when m- accumulative pin
Sequence is measured, determines the multiple groups parameter of the iunction for curve, wherein the parameter is corresponding with the time；According to the multiple groups
Parameter and return of goods record, are trained preset estimation model.
Optionally, the turnover information determination module is also used to: according to the sales volume data, the determining and current time
Corresponding parameter；The parameter is inputted into preset estimation model, with the turnover information of the determination end article.
Optionally, the iunction for curve is shown below:
Y=axb
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are ginseng to be determined
Number.
Optionally, the model training module is also used to: the iunction for curve being done Logarithm conversion, by the song
Line fitting function is converted to linear function；Based on it is described when m- accumulative sales volume sequence and linear function, using least square method,
Determine the multiple groups parameter of the iunction for curve.
Optionally, the preset estimation model is Logic Regression Models.
To achieve the above object, another aspect according to an embodiment of the present invention, provides a kind of electronic equipment, comprising: one
A or multiple processors；Storage device, for storing one or more programs, when one or more of programs are one
Or multiple processors execute, so that one or more of processors realize the related information generation method of the embodiment of the present invention.
To achieve the above object, according to an embodiment of the present invention in another aspect, provide a kind of computer-readable medium,
On be stored with computer program, when described program is executed by processor realize the embodiment of the present invention related information generation method.
One embodiment in foregoing invention has the following advantages that or the utility model has the advantages that because using current based on end article
The sales volume data of time and preset estimation model, determine the turnover information of the end article；It is carried out according to the turnover information
The technological means of stock control can reasonably manage inventory, and then improve whole efficiency of operation, reduce warehouse cost and fund
Cost of possession reduces manual intervention, reduces human cost.
Further effect possessed by above-mentioned non-usual optional way adds hereinafter in conjunction with specific embodiment
With explanation.
Detailed description of the invention
Attached drawing for a better understanding of the present invention, does not constitute an undue limitation on the present invention.Wherein:
Fig. 1 is the schematic diagram of the main flow of related information generation method according to an embodiment of the invention；
Fig. 2 is that the history of certain seasonal merchandise adds up sales volume curve graph；
Fig. 3 is the curve matching schematic diagram of the m- accumulative sales volume sequence of clock synchronization of the embodiment of the present invention；
Fig. 4 is the visualization schematic diagram of the multiple groups parameter of the iunction for curve of the embodiment of the present invention；
Fig. 5 is the schematic diagram of the main flow of related information generation method according to another embodiment of the present invention；
Fig. 6 is the schematic diagram of the main modular of related information generating means according to an embodiment of the present invention；
Fig. 7 is that the embodiment of the present invention can be applied to exemplary system architecture figure therein；
Fig. 8 is adapted for the structural representation of the computer system for the terminal device or server of realizing the embodiment of the present invention
Figure.
Specific embodiment
Below in conjunction with attached drawing, an exemplary embodiment of the present invention will be described, including the various of the embodiment of the present invention
Details should think them only exemplary to help understanding.Therefore, those of ordinary skill in the art should recognize
It arrives, it can be with various changes and modifications are made to the embodiments described herein, without departing from scope and spirit of the present invention.Together
Sample, for clarity and conciseness, descriptions of well-known functions and structures are omitted from the following description.
Fig. 1 is the schematic diagram of the main flow of related information generation method according to an embodiment of the present invention.As shown in Figure 1,
This method comprises:
Step S101: the sales volume data of end article current time are obtained；
Step S102: it is based on the sales volume data and preset estimation model, determines the turnover information of the end article；
Step S103: according to the turnover information, the related information of the end article is generated.
In above embodiment, for step S101, current time can be with " day " for unit, i.e. acquisition end article is worked as
The sales volume data of day.Current time can also be with " week " for unit, and the present invention is herein with no restrictions.
It should be noted that above-mentioned sales volume data can be end article in the sales data of a storehouse.Because of this hair
Bright embodiment in view of same commodity in different regions it is possible that sales volume variation, such as winter clothing is southern and northern
Sales variance, so, for above-mentioned sales volume data are both for single storehouse, i.e., when same commodity appear in different storehouses, respectively
A storehouse carries out subsequent calculating according to the sales volume data of its own, determines the commodity in the return of goods information of the storehouse, such as in library
Commodity H is all stored in room A and storehouse B, according to commodity H in the sales volume data (such as outbound amount) of storehouse A, determines that commodity H exists
The return of goods information of storehouse A；According to commodity H in the sales volume data (such as outbound amount) of storehouse B, determine commodity H in the return of goods of storehouse B
Information.It finally can be by summarizing the return of goods information of each storehouse, the commodity data finally returned goods.
In an alternate embodiment of the invention, above-mentioned end article can be seasonal merchandise.In practical applications, commodity are in supply and demand
There is certain seasonal variety features for level, i.e., as the growth trend of the conversion commodity supply in season or demand is relatively solid
It is fixed, there is seasonal fluctuation characteristic, this fluctuation characteristic is known as seasonal fluctuation rule or seasonal characteristics, there will be season
The commodity of section property feature are known as seasonal merchandise.And seasonal merchandise reaches sale within a sales cycle once or twice
Extra inventory usually can be returned supplier in the period of slack sales by peak.Season can be determined according to the method for the embodiment of the present invention
Whether section property commodity enter whether the period of slack sales or the seasonal merchandise are unseasonable goods, when determining that the seasonal merchandise enters pin
Sell dull season or determine the seasonal merchandise be unseasonable goods after, when can further determine the return of goods of the seasonal merchandise
Between.
For step S102, above-mentioned turnover information may include whether the end article is unseasonable goods or the end article
It is the probability of unseasonable goods, if it is unseasonable goods, which can also include sale speed, unsalable degree (such as one
As it is unsalable, serious unsalable) etc. other informations.Certainly, above-mentioned turnover information can also include whether the end article is situation of selling well quotient
Product or the end article are the probability of best-selling product, and if it is best-selling product, which can also include sale speed, smooth
The other informations such as pin degree (such as general situation of selling well, very situation of selling well).It in the present embodiment, whether is unseasonable goods with end article
For be illustrated, specifically, it is by judging that the end article is the general of unseasonable goods that whether end article, which is unseasonable goods,
Whether rate is greater than probability threshold value to determine.
In the present embodiment, above-mentioned preset estimation model can be Logic Regression Models.
Logistic regression (logistic regression) is a kind of generalized linear regression (generalized linear
Model), the dependent variable of logistic regression can be two classification, be also possible to it is polytypic, but two classification it is more commonly used,
Also easier to understand, the estimation model of the embodiment of the present invention is exactly the Logic Regression Models of two classification.
The logistic regression of two classification is probability for calculating " event=success " and " event=failure ", work as because
When the type of variable belongs to binary (such as 1/0, true/false, Yes/No) variable, logistic regression can be used.It is returned when establishing logic
Return after model, can use the model prediction under different independents variable, occur certain situation probability have it is much.In reality
It in application scenarios, is analyzed from sales volume angle, a commodity or is unseasonable goods or is not unseasonable goods.Therefore, judge
The problem of whether one commodity is unseasonable goods can be regarded as two classification problems, in turn, used in the embodiment of the present invention
Logic Regression Models can be the Logic Regression Models of two classification.
In step s 102, after the probability that end article is unseasonable goods is calculated, the probability that can will obtain
It is compared with probability threshold value, if the probability being calculated is greater than threshold value, can be determined that the end article is unseasonable goods, it will
The end article stamps unsalable label, so as to subsequent processing；It, can be with if the probability being calculated is less than or equal to probability threshold value
Determine that the end article is not unseasonable goods.In an alternate embodiment of the invention, which can be 0.5.
For step S103, the related information of end article may include replenish information, return of goods information, sales promotion information etc. its
His information.It is raw according to the turnover information such as when it is best-selling product that the turnover information for determining end article, which is the end article,
At related information can be thought as the information that replenishes of end article, can further be mended according to the information that replenishes is appropriate
Goods；When it is unseasonable goods that the turnover information for determining end article, which is the end article, according to the association of turnover information generation
Information can be thought as the return of goods information of end article, and further, return of goods information may include time point of returning goods, and can also include
Return of goods quantity, so as to carry out the return of goods appropriate in the suitable time；When the turnover information for determining end article is the target
When commodity are unseasonable goods, the sales promotion information of end article can be thought as according to the related information that the turnover information generates, into one
Step, sales promotion information may include the promotion duration, can also include promotion method, so as to suitable in the suitable period
Locality is promoted.It is illustrated for determining that end article is unseasonable goods below: when determining that end article is unsalable quotient
When product, that is, when the probability of end article is calculated greater than 0.5, if the probability is bigger, the return of goods time of the end article
It is more early.For example, the probability be 0.8, then can will the same day as return of goods time announcement administrative staff to carry out return of goods operation；When this
Probability be 0.5 when, can first without return goods operate, next day (such as the date on the same day be on December 3rd, 2017, Xia Yi
For on December 4th, 2017) the newly-increased sales data of basis calculates probability again, it, can be by next day (2017 if probability increases
On December 4, in) it is used as return of goods time announcement administrative staff to carry out return of goods operation.When being returned goods according to the determine the probability being calculated
Between concrete mode can flexible setting according to actual needs, the present invention is herein with no restrictions.
In an alternate embodiment of the invention, the method also includes saving the sales volume data of end article current time, with training
Preset estimation model, to update the model.
The method of the embodiment of the present invention, according to the sales volume data of end article current time and preset estimation model, energy
The enough accurate turnover information for determining commodity, the related information of the end article is generated according to the turnover information, is believed according to the association
Breath can reasonably manage inventory, and then improve whole efficiency of operation, reduce warehouse cost and cost of capital, reduce artificial
Intervene, reduces human cost.Further, the method for the embodiment of the present invention is according to the sales volume data of commodity current time and default
Estimation model, the probability that end article is unseasonable goods is calculated, according to the return of goods time of the determine the probability end article, energy
The enough return of goods time for accurately calculating commodity, manual intervention is reduced, improve intelligent level, improved whole efficiency of operation, reduce
Warehouse cost and cost of capital.Moreover, the sales volume data of above-mentioned current time can be for same storehouse, solve
Different regions seasonality different problems.
It is worth noting that related information of embodiment of the present invention generation method can not only be applied to e-commerce field,
Other objects with periodicity or seasonal characteristics can also be applied, such as (northern summer electricity consumption is more, and winter is used for electricity consumption
Electricity decline), the volume of the flow of passengers etc..By taking electricity consumption as an example, the electricity consumption of the available current time of step S101, step S102 can be with
According to the electricity consumption of current time and preset estimation model, electricity consumption information is determined, step S103 can be according to the electricity consumption
Amount information is managed, such as predicts the power supply volume of next period or next stage.
In an alternate embodiment of the invention, above-mentioned preset estimation model can be obtained according to following process:
Obtain the history sales volume data of the end article and record of returning goods；
According to the history sales volume data and record of returning goods, preset estimation model is trained.
Wherein, for above-mentioned history sales volume data and the return of goods are recorded both for single storehouse.The embodiment of the present invention according to
The history sales volume data of end article and the preset estimation model of record training of returning goods, judge target quotient according to the estimation model
Whether product are unseasonable goods, and asking for time of returning goods cannot accurately be determined by solving to set in the prior art caused by preset parameter
Topic.
Further, according to the history sales volume data and record of returning goods, the step that preset estimation model is trained
Suddenly include:
According to the history sales volume data, when building m- accumulative sales volume sequence；
To it is described when m- accumulative sales volume sequence carry out curve fitting, determine iunction for curve；
Based on it is described when m- accumulative sales volume sequence, determine the multiple groups parameter of the iunction for curve, wherein the ginseng
Number is corresponding with the time；
It is recorded according to the multiple groups parameter and the return of goods, preset estimation model is trained.
Wherein, m- accumulative sales volume sequence refers to the accumulative sales volume of every day in end article preset time period when above-mentioned
Ordered series of numbers made of in chronological sequence sequence arranges
As specific example, sales volume data of the available end article in nearly 2 years of some storehouse, such as the following table 1
Shown (calculating for convenience, it is assumed that monthly 30 days):
Table 1:
| Date | 2016-1-1 | 2016-1-2 | 2016-1-3 | 2016-1-4 | … | 2016-3-1 | 2016-3-2 |
| Number of days | 1 | 2 | 3 | 4 | … | 60 | 61 |
| Sales volume | S1 | S2 | S3 | S4 | … | S60 | S61 |
From the 60th day, first 60 days accumulative sales volumes are obtained, m- accumulative sales volume sequence when constructing is as shown in table 2 below:
Table 2:
| Date | Number of days | Accumulative sales volume sequence |
| 2016-3-1 | 60 | (S1,S1+S2,S1+S2+S3……,S1+S2+……+S60) |
| 2016-3-2 | 61 | (S2,S2+S3,S2+S3+S4……,S2+S3+……+S61) |
| 2016-3-3 | 62 | (S3,S3+S4,S3+S4+S5……,S3+S4+……+S62) |
As shown in Table 2, having a length daily from the 60th day is 60 days accumulative sales volume sequences.
Then, the history sales volume data of end article are depicted on coordinate system, accumulative sales volume curve graph are drawn, according to this
The regularity of distribution of a little data points and the variation tendency of accumulative sales volume select suitable iunction for curve.As specific example,
Please refer to Fig. 2.Fig. 2 is that the history of certain seasonal merchandise adds up sales volume curve graph.
According to fig. 2, the sales cycle of seasonal merchandise can be defined as three phases: rise period, decline phase and stabilization
Phase.In addition, as can be seen from the figure busy season and dull season of the commodity within a sales cycle.Moreover, the time of preferably returning goods is
The commodity busy season terminates, when adding up the decline of sales volume speedup to a certain extent.By Fig. 2 it was determined that above-mentioned iunction for curve is as follows
Shown in formula (3):
Y=axb (3)
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are ginseng to be determined
Number.
Logarithm conversion is done to formula (3) left and right respectively, is converted into linear function ln (y)=ln (b) ln (x)+ln
(a), with least square method, (least square method is a kind of mathematical optimization techniques, it finds number by minimizing the quadratic sum of error
According to optimal function matching) solve obtain the value of coefficient a and b, using coefficient a and b as parameter.
Then, m- accumulative sales volume sequence and least square method, determine the multiple groups parameter of the linear function such as when being based on described
Shown in the following table 3:
Table 3:
| Date | Number of days | Accumulative sales volume sequence | Parameter (a, b) |
| 2016-3-1 | 60 | (S1,S1+S2,S1+S2+S3……,S1+S2+……+S60) | (a1,b1) |
| 2016-3-2 | 61 | (S2,S2+S3,S2+S3+S4……,S2+S3+……+S61) | (a2,b2) |
| 2016-3-3 | 62 | (S3,S3+S4,S3+S4+S5……,S3+S4+……+S62) | (a3,b3) |
Wherein, curve-fitting results are referring to FIG. 3, from the figure 3, it may be seen that parameter a reflects the accumulative pin that commodity are gone over 60 days
Amount, i.e. parameter a are to indicate that the commodity add up the feature of sales volume, and when accumulative sales volume is higher, the value of a is bigger；Parameter b then represents accumulative
The variation tendency of sales volume, i.e. parameter b are to indicate that the commodity add up the feature of sales volume speedup, and when b is approximately equal to 0, sales volume is stablized, when
When b>0, the more big accumulative sales volume growth rate of b is faster (corresponding busy season sales volume increase), and when b<0, the smaller accumulative sales volume of b increases
Speed is slower (corresponding period of slack sales sales volume growth recession).
The multiple groups parameter of acquisition is visualized, as shown in Figure 4.As shown in Figure 4, when commodity enter the period of slack sales,
Sales volume rapidly increases so that a reaches peak value, simultaneously as dull season adds up the decline of sales volume speedup when being in the busy season due to the past 60 days
The reason of b drop to valley.Therefore, the indication of goods for entering the period of slack sales is unseasonable goods by the embodiment of the present invention, is given corresponding
Return of goods behavior.
According to the multiple groups parameter, the process being trained to preset estimation model is as follows:
From all return of goods record obtained in data warehouse in the commodity nearly 2 years, if the same day has the return of goods to record, by the same day
Labeled as " 1 ", it is expressed as positive sample, the date stamp for record of not returning goods is " 0 ", is expressed as negative sample.
Then, the multiple groups parameter and operation label of returning goods are associated, to construct sample training collection.For example, training
Sample set D=[(a1, b1,1), (a2, b2,0), (a3, b3,0) ... (aN, bN, 1)], wherein N be just more than or equal to 1
Integer.
In practical application scene, it is possible that the unbalanced problem of positive and negative sample size, such as the quantity of negative sample
Be far longer than the quantity of positive sample, then it can be by way of the positive negative sample of handmarking or using over-sampling algorithm to positive sample
The quantity of the mode sampled, the quantity and negative sample that make positive sample is close.
Over-sampling is carried out to positive sample specifically, can use SMOTE algorithm, and F1score is used to comment as performance
Valence index.Wherein, F1score is a kind of index for being used to measure two disaggregated model accuracy in statistics, and F1score is accurate
The harmomic mean of rate and recall rate is that F1score also can be very high when accurate rate is all very high.
The step of based on training sample set training preset estimation model, is as follows:
(1) construction logic regression model is shown below:
In embodiments of the present invention, it is (i.e. a) and tired for the accumulative sales volume feature of commodity to influence the variable of merchandise return time
Count sales volume speedup feature (i.e. b), therefore, the Logic Regression Models of building are the linear combination of this 2 variables, then obtain as
(3) Logic Regression Models shown in formula.Wherein, x1And x2Indicate above-mentioned two variable, θ0、θ1And θ2For coefficient to be determined.
(2) scanned samples training set, solving model parameter.
Specifically, the process of solving model parameter is to minimize the process of loss function, wherein loss function such as following formula institute
Show:
Do not consider to add up and ask local derviation to obtain coefficient update direction for each coefficient:
Scanned samples, iteration above-mentioned formula can acquire coefficientWherein, j=0,
1,2.The coefficient of acquisition is substituted into formula (3), then obtains preset estimation model.
Fig. 5 is the schematic diagram of the main flow of the related information generation method of another embodiment of the present invention.As shown in figure 5,
This method comprises:
Step S501: the sales volume data of end article current time are obtained；
Step S502: according to the sales volume data, parameter corresponding with the current time is determined；
Step S503: inputting preset estimation model for the parameter, and calculating the end article is the general of unseasonable goods
Rate；
Step S504: according to the probability, the return of goods time of the end article is determined.
In above embodiment, step S501 is referring to the step S101 in Fig. 1, and details are not described herein by the present invention.
For step S502, according to 59 days before the sales volume data and current time of current time sales volume data, generate with
M- accumulative sales volume sequence when current time is corresponding calculates parameter corresponding with current time.
The parameter being calculated is inputted into preset estimation model, obtains the probability that the end article is poor seller, then
According to the return of goods time of the determine the probability end article.
The method of the embodiment of the present invention can accurately calculate the return of goods time of commodity, reduce manual intervention, improve intelligence
Change horizontal, the whole efficiency of operation of raising, reduction warehouse cost and cost of capital.Moreover, the sales volume number of above-mentioned current time
According to being for same storehouse, therefore, the method for the embodiment of the present invention can be according to current year seasonal merchandise in different regions
Sales situation, the seasonal merchandise is recognized accurately in which area and enters the period of slack sales, which area does not enter into pin
It sells dull season, and then is unseasonable goods by the indication of goods in the period of slack sales is entered, give corresponding return of goods behavior, solve differently
Area's seasonality different problems.
Fig. 6 is the schematic diagram of the main modular of related information generating means according to an embodiment of the present invention.As shown in fig. 6,
The device 600 includes:
Sales volume data acquisition module 601, for obtaining the sales volume data of end article current time；
Information determination module 602 is had enough to meet the need, for being based on the sales volume data and preset estimation model, determines the target
The turnover information of commodity；
Related information generation module 603, for generating the related information of the end article according to the turnover information.
Optionally, described device further includes model training module, is used for: obtaining the history sales volume data of the end article
It is recorded with returning goods；According to the history sales volume data and record of returning goods, preset estimation model is trained.
Optionally, the model training module is also used to: according to the history sales volume data, when building m- accumulative sales volume
Sequence；To it is described when m- accumulative sales volume sequence carry out curve fitting, determine iunction for curve；Based on it is described when m- accumulative pin
Sequence is measured, determines the multiple groups parameter of the iunction for curve, wherein the parameter is corresponding with the time；According to the multiple groups
Parameter and return of goods record, are trained preset estimation model.
Optionally, the turnover information determination module 602 is also used to: determining and described current according to the sales volume data
Time corresponding parameter；The parameter is inputted into preset estimation model, determines the turnover information of the end article.
Optionally, the iunction for curve is shown below:
Y=axb
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are ginseng to be determined
Number.
Optionally, the model training module is also used to: the iunction for curve being done Logarithm conversion, by the song
Line fitting function is converted to linear function；Based on it is described when the m- accumulative sales volume sequence and linear function, utilize least square
Method determines the multiple groups parameter of the iunction for curve.
Optionally, the preset estimation model is Logic Regression Models.
Method provided by the embodiment of the present invention can be performed in above-mentioned apparatus, has the corresponding functional module of execution method and has
Beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to method provided by the embodiment of the present invention.
Fig. 7, which is shown, can apply the related information generation method of the embodiment of the present invention or showing for related information generating means
Example property system architecture 700.
As shown in fig. 7, system architecture 700 may include terminal device 701,702,703, network 704 and server 705.
Network 704 between terminal device 701,702,703 and server 705 to provide the medium of communication link.Network 704 can be with
Including various connection types, such as wired, wireless communication link or fiber optic cables etc..
User can be used terminal device 701,702,703 and be interacted by network 704 with server 705, to receive or send out
Send message etc..Various telecommunication customer end applications, such as the application of shopping class, net can be installed on terminal device 701,702,703
The application of page browsing device, searching class application, instant messaging tools, mailbox client, social platform software etc..
Terminal device 701,702,703 can be the various electronic equipments with display screen and supported web page browsing, packet
Include but be not limited to smart phone, tablet computer, pocket computer on knee and desktop computer etc..
Server 705 can be to provide the server of various services, such as utilize terminal device 701,702,703 to user
The shopping class website browsed provides the back-stage management server supported.Back-stage management server can believe the product received
The data such as breath inquiry request carry out the processing such as analyzing, and processing result (such as target push information, product information) is fed back to
Terminal device.
It should be noted that related information generation method provided by the embodiment of the present invention is generally executed by server 705,
Correspondingly, related information generating means are generally positioned in server 705.
It should be understood that the number of terminal device, network and server in Fig. 7 is only schematical.According to realization need
It wants, can have any number of terminal device, network and server.
Below with reference to Fig. 8, it illustrates the computer systems 800 for the terminal device for being suitable for being used to realize the embodiment of the present invention
Structural schematic diagram.Terminal device shown in Fig. 8 is only an example, function to the embodiment of the present invention and should not use model
Shroud carrys out any restrictions.
As shown in figure 8, computer system 800 includes central processing unit (CPU) 801, it can be read-only according to being stored in
Program in memory (ROM) 802 or be loaded into the program in random access storage device (RAM) 803 from storage section 808 and
Execute various movements appropriate and processing.In RAM 803, also it is stored with system 800 and operates required various programs and data.
CPU 801, ROM 802 and RAM 803 are connected with each other by bus 804.Input/output (I/O) interface 805 is also connected to always
Line 804.
I/O interface 805 is connected to lower component: the importation 806 including keyboard, mouse etc.；It is penetrated including such as cathode
The output par, c 807 of spool (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section 808 including hard disk etc.；
And the communications portion 809 of the network interface card including LAN card, modem etc..Communications portion 809 via such as because
The network of spy's net executes communication process.Driver 810 is also connected to I/O interface 805 as needed.Detachable media 811, such as
Disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 810, in order to read from thereon
Computer program be mounted into storage section 808 as needed.
Particularly, disclosed embodiment, the process described above with reference to flow chart may be implemented as counting according to the present invention
Calculation machine software program.For example, embodiment disclosed by the invention includes a kind of computer program product comprising be carried on computer
Computer program on readable medium, the computer program include the program code for method shown in execution flow chart.?
In such embodiment, which can be downloaded and installed from network by communications portion 809, and/or from can
Medium 811 is dismantled to be mounted.When the computer program is executed by central processing unit (CPU) 801, system of the invention is executed
The above-mentioned function of middle restriction.
It should be noted that computer-readable medium shown in the present invention can be computer-readable signal media or meter
Calculation machine readable storage medium storing program for executing either the two any combination.Computer readable storage medium for example can be --- but not
Be limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor system, device or device, or any above combination.Meter
The more specific example of calculation machine readable storage medium storing program for executing can include but is not limited to: have the electrical connection, just of one or more conducting wires
Taking formula computer disk, hard disk, random access storage device (RAM), read-only memory (ROM), erasable type may be programmed read-only storage
Device (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), light storage device, magnetic memory device,
Or above-mentioned any appropriate combination.In the present invention, computer readable storage medium can be it is any include or storage journey
The tangible medium of sequence, the program can be commanded execution system, device or device use or in connection.And at this
In invention, computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.Include on computer-readable medium
Program code can transmit with any suitable medium, including but not limited to: wireless, electric wire, optical cable, RF etc. are above-mentioned
Any appropriate combination.
Flow chart and block diagram in attached drawing are illustrated according to the system of various embodiments of the invention, method and computer journey
The architecture, function and operation in the cards of sequence product.In this regard, each box in flowchart or block diagram can generation
A part of one module, program segment or code of table, a part of above-mentioned module, program segment or code include one or more
Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box
The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical
On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants
It is noted that the combination of each box in block diagram or flow chart and the box in block diagram or flow chart, can use and execute rule
The dedicated hardware based systems of fixed functions or operations is realized, or can use the group of specialized hardware and computer instruction
It closes to realize.
Being described in module involved in the embodiment of the present invention can be realized by way of software, can also be by hard
The mode of part is realized.Described module also can be set in the processor, for example, can be described as: a kind of processor packet
It includes sending module, obtain module, determining module and first processing module.Wherein, the title of these modules is under certain conditions simultaneously
The restriction to the unit itself is not constituted, for example, sending module is also described as " sending picture to the server-side connected
The module of acquisition request ".
As on the other hand, the present invention also provides a kind of computer-readable medium, which be can be
Included in equipment described in above-described embodiment；It is also possible to individualism, and without in the supplying equipment.Above-mentioned calculating
Machine readable medium carries one or more program, when said one or multiple programs are executed by the equipment, makes
Obtaining the equipment includes:
Obtain the sales volume data of end article current time；
Based on the sales volume data and preset estimation model, the turnover information of the end article is determined；
According to the turnover information, the related information of the end article is generated.
The method of the embodiment of the present invention, according to the sales volume data of end article current time and preset estimation model, energy
The enough accurate turnover information for determining commodity, the related information of end article is generated according to the turnover information, according to the related information
Inventory can be reasonably managed, and then improves whole efficiency of operation, reduces warehouse cost and cost of capital, is reduced artificial dry
In advance, human cost is reduced.
Above-mentioned specific embodiment, does not constitute a limitation on the scope of protection of the present invention.Those skilled in the art should be bright
It is white, design requirement and other factors are depended on, various modifications, combination, sub-portfolio and substitution can occur.It is any
Made modifications, equivalent substitutions and improvements etc. within the spirit and principles in the present invention, should be included in the scope of the present invention
Within.
Claims (16)
1. a kind of related information generation method characterized by comprising
Obtain the sales volume data of end article current time；
Based on the sales volume data and preset estimation model, the turnover information of the end article is determined；
According to the turnover information, the related information of the end article is generated.
2. the method according to claim 1, wherein be based on the sales volume data and preset estimation model,
Before the turnover information for determining the end article, the method also includes:
Obtain the history sales volume data of the end article and record of returning goods；
According to the history sales volume data and record of returning goods, preset estimation model is trained.
3. according to the method described in claim 2, it is characterized in that, according to the history sales volume data and record of returning goods, to pre-
If estimation model be trained and include:
According to the history sales volume data, when building m- accumulative sales volume sequence；
To it is described when m- accumulative sales volume sequence carry out curve fitting, determine iunction for curve；
Based on it is described when m- accumulative sales volume sequence, determine the multiple groups parameter of the iunction for curve, wherein the parameter with
Time is corresponding；
It is recorded according to the multiple groups parameter and the return of goods, preset estimation model is trained.
4. according to the method described in claim 3, it is characterized in that, described be based on the sales volume data and preset estimation mould
Type determines that the turnover information of the end article includes:
According to the sales volume data, parameter corresponding with the current time is determined；
The parameter is inputted into preset estimation model, with the turnover information of the determination end article.
5. requiring the method according to claim 3, which is characterized in that the iunction for curve is shown below:
Y=axb
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are parameter to be determined.
6. according to the method described in claim 5, it is characterized in that, the multiple groups parameter of the iunction for curve is according to following mistake
Journey determines:
The iunction for curve is done into Logarithm conversion, the iunction for curve is converted into linear function；
Based on it is described when the m- accumulative sales volume sequence and linear function, determine the curve matching letter using least square method
Several multiple groups parameters.
7. method according to claim 1-6, which is characterized in that the preset estimation model is logistic regression
Model.
8. a kind of related information generating means, are characterized in that, comprising:
Sales volume data acquisition module, for obtaining the sales volume data of end article current time；
Information determination module is had enough to meet the need, for being based on the sales volume data and preset estimation model, determines the end article
Have enough to meet the need information；
Related information generation module, for generating the related information of the end article according to the turnover information.
9. device according to claim 8, which is characterized in that described device further includes model training module, is used for:
Obtain the history sales volume data of the end article and record of returning goods；
According to the history sales volume data and record of returning goods, preset estimation model is trained.
10. device according to claim 9, which is characterized in that the model training module is also used to:
According to the history sales volume data, when building m- accumulative sales volume sequence；
To it is described when m- accumulative sales volume sequence carry out curve fitting, determine iunction for curve；
Based on it is described when m- accumulative sales volume sequence, determine the multiple groups parameter of the iunction for curve, wherein the parameter with
Time is corresponding；
It is recorded according to the multiple groups parameter and the return of goods, preset estimation model is trained.
11. device according to claim 10, which is characterized in that the turnover information determination module is also used to:
According to the sales volume data, parameter corresponding with the current time is determined；
The parameter is inputted into preset estimation model, with the turnover information of the determination end article.
12. 0 requiring the device according to claim 1, which is characterized in that the iunction for curve is shown below:
Y=axb
Wherein, x indicates the time, and y indicates the accumulative sales volume data of end article when the time is x, and a and b are parameter to be determined.
13. device according to claim 11, which is characterized in that the model training module is also used to:
The iunction for curve is done into Logarithm conversion, the iunction for curve is converted into linear function；
Based on it is described when m- accumulative sales volume sequence and linear function, determine the iunction for curve using least square method
Multiple groups parameter.
14. according to the described in any item devices of claim 8-13, which is characterized in that the preset estimation model returns for logic
Return model.
15. a kind of electronic equipment characterized by comprising
One or more processors；
Storage device, for storing one or more programs,
When one or more of programs are executed by one or more of processors, so that one or more of processors are real
The now method as described in any in claim 1-7.
16. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that described program is held by processor
The method as described in any in claim 1-7 is realized when row.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711420197.4A CN109961198B (en) | 2017-12-25 | 2017-12-25 | Associated information generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711420197.4A CN109961198B (en) | 2017-12-25 | 2017-12-25 | Associated information generation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109961198A true CN109961198A (en) | 2019-07-02 |
| CN109961198B CN109961198B (en) | 2021-12-31 |
Family
ID=67020875
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711420197.4A Active CN109961198B (en) | 2017-12-25 | 2017-12-25 | Associated information generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109961198B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348635A (en) * | 2019-07-12 | 2019-10-18 | 创新奇智(成都)科技有限公司 | Intelligent replenishing method, storage medium, system and device based on end-to-end study |
| CN111985967A (en) * | 2020-08-17 | 2020-11-24 | 北京每日优鲜电子商务有限公司 | Article information generation method and device, electronic equipment and computer readable medium |
| CN113706262A (en) * | 2021-09-02 | 2021-11-26 | 深圳市坤同智能仓储科技有限公司 | Automatic goods channel configuration method for goods shelf type goods and novel goods shelf |
| CN113762582A (en) * | 2021-01-29 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Data processing method and device for determining goods returning amount based on inventory simulation |
| CN113762858A (en) * | 2020-11-24 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Inventory management method and device |
| CN114049072A (en) * | 2022-01-11 | 2022-02-15 | 北京京东振世信息技术有限公司 | Index determination method and device, electronic equipment and computer readable medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105045819A (en) * | 2015-06-26 | 2015-11-11 | 深圳市腾讯计算机系统有限公司 | Model training method and device for training data |
| US20160189177A1 (en) * | 2014-12-29 | 2016-06-30 | DecisionGPS, LLC | Determination of a Purchase Recommendation |
| CN106022915A (en) * | 2016-07-01 | 2016-10-12 | 中国民生银行股份有限公司 | Enterprise credit risk assessment method and apparatus |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
| CN107025565A (en) * | 2016-01-29 | 2017-08-08 | 北京京东尚科信息技术有限公司 | A kind of method and system for improving e-commerce website conversion ratio |
| CN107358385A (en) * | 2017-06-16 | 2017-11-17 | 深圳市盛路物联通讯技术有限公司 | A kind of method and server of merchandise of automatic vending machine dispatching |
-
2017
- 2017-12-25 CN CN201711420197.4A patent/CN109961198B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160189177A1 (en) * | 2014-12-29 | 2016-06-30 | DecisionGPS, LLC | Determination of a Purchase Recommendation |
| CN105045819A (en) * | 2015-06-26 | 2015-11-11 | 深圳市腾讯计算机系统有限公司 | Model training method and device for training data |
| CN107025565A (en) * | 2016-01-29 | 2017-08-08 | 北京京东尚科信息技术有限公司 | A kind of method and system for improving e-commerce website conversion ratio |
| CN106022915A (en) * | 2016-07-01 | 2016-10-12 | 中国民生银行股份有限公司 | Enterprise credit risk assessment method and apparatus |
| CN106971249A (en) * | 2017-05-05 | 2017-07-21 | 北京挖玖电子商务有限公司 | A kind of Method for Sales Forecast and replenishing method |
| CN107358385A (en) * | 2017-06-16 | 2017-11-17 | 深圳市盛路物联通讯技术有限公司 | A kind of method and server of merchandise of automatic vending machine dispatching |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348635A (en) * | 2019-07-12 | 2019-10-18 | 创新奇智(成都)科技有限公司 | Intelligent replenishing method, storage medium, system and device based on end-to-end study |
| CN111985967A (en) * | 2020-08-17 | 2020-11-24 | 北京每日优鲜电子商务有限公司 | Article information generation method and device, electronic equipment and computer readable medium |
| CN113762858A (en) * | 2020-11-24 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Inventory management method and device |
| CN113762582A (en) * | 2021-01-29 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | Data processing method and device for determining goods returning amount based on inventory simulation |
| CN113706262A (en) * | 2021-09-02 | 2021-11-26 | 深圳市坤同智能仓储科技有限公司 | Automatic goods channel configuration method for goods shelf type goods and novel goods shelf |
| CN114049072A (en) * | 2022-01-11 | 2022-02-15 | 北京京东振世信息技术有限公司 | Index determination method and device, electronic equipment and computer readable medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109961198B (en) | 2021-12-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109961198A (en) | Related information generation method and device | |
| CN110555578B (en) | Sales prediction method and device | |
| CN109840734A (en) | Information output method and device | |
| CN113095893A (en) | Method and device for determining sales of articles | |
| CN113128932B (en) | Warehouse stock processing method and device, storage medium and electronic equipment | |
| CN110209677A (en) | The method and apparatus of more new data | |
| CN107274209A (en) | The method and apparatus for predicting advertising campaign sales data | |
| CN109214730A (en) | Information-pushing method and device | |
| CN110020143A (en) | A kind of landing page generation method and device | |
| CN110109901A (en) | The method and apparatus for screening target object | |
| CN109961299A (en) | The method and apparatus of data analysis | |
| CN110276652A (en) | Method and apparatus for pushed information | |
| CN108898496A (en) | Products Show method, apparatus, medium and electronic equipment | |
| CN110348921A (en) | The method and apparatus that shops's article is chosen | |
| CN110097302A (en) | The method and apparatus for distributing order | |
| CN110019367A (en) | A kind of method and apparatus of statistical data feature | |
| CN113988768B (en) | Inventory determination method and device | |
| CN110020876A (en) | A kind of information generating method and device | |
| CN109934523A (en) | Information processing method and device | |
| CN109902847A (en) | Prediction divides the method and apparatus of library order volume | |
| CN112348590A (en) | Method and device for determining value of article, electronic equipment and storage medium | |
| CN113450042A (en) | Method and device for determining replenishment quantity | |
| CN110956478A (en) | Method and device for determining goods input quantity | |
| CN110008970A (en) | A kind of address information generation method and device | |
| CN112989276A (en) | Evaluation method and device of information push system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |